# """Inference-only Yi-VL model."""

# import os
# from typing import List, Optional

# import torch
# import torch.nn as nn
# from sglang.srt.models.llava import (
#     LlavaLlamaForCausalLM,
#     clip_vision_embed_forward,
#     monkey_path_clip_vision_embed_forward,
# )
# from transformers import CLIPVisionModel, LlavaConfig
# from vllm.model_executor.weight_utils import (
#     default_weight_loader,
#     hf_model_weights_iterator,
# )


# class YiVLForCausalLM(LlavaLlamaForCausalLM):
#     def __init__(self, *args, **kwargs):
#         self.config = kwargs["config"]
#         super().__init__(self.config)

#         self.multi_modal_projector = YiVLMultiModalProjector(self.config)
#         self.vision_tower_subfolder = self.config.mm_vision_tower.replace(
#             "./", ""
#         )  # Everything after "./"

#     def load_weights(
#         self,
#         model_name_or_path: str,
#         cache_dir: Optional[str] = None,
#         load_format: str = "auto",
#         revision: Optional[str] = None,
#     ):
#         # We have to use the subfolder of the main model directory (e.g. 01-ai/Yi-VL-6B)
#         self.vision_tower = CLIPVisionModel.from_pretrained(
#             model_name_or_path,
#             torch_dtype=torch.float16,
#             subfolder=self.vision_tower_subfolder,
#         ).cuda()

#         self.vision_tower.eval()

#         self.vision_feature_layer = self.config.mm_vision_select_layer
#         self.vision_feature_select_strategy = self.config.mm_vision_select_feature
#         self.image_size = self.vision_tower.config.image_size
#         self.patch_size = self.vision_tower.config.patch_size

#         self.mm_patch_merge_type = getattr(self.config, "mm_patch_merge_type", "flat")
#         self.image_aspect_ratio = getattr(self.config, "image_aspect_ratio", "square")
#         self.image_grid_pinpoints = getattr(self.config, "image_grid_pinpoints", None)

#         self.image_feature_len = int((self.image_size / self.patch_size) ** 2)
#         if self.vision_feature_select_strategy == "patch":
#             pass
#         elif self.vision_feature_select_strategy == "cls_patch":
#             self.image_feature_len += 1
#         else:
#             raise ValueError(f"Unexpected select feature: {self.select_feature}")

#         # load mm_projector
#         # TODO: support TP?
#         projector_weights = {
#             "model.mm_projector.0": "multi_modal_projector.linear_1",
#             "model.mm_projector.1": "multi_modal_projector.ln_1",
#             "model.mm_projector.3": "multi_modal_projector.linear_2",
#             "model.mm_projector.4": "multi_modal_projector.ln_2",
#             "model.vision_tower.vision_tower": "vision_tower",  # Update the vision tower weights if we find them in the checkpoint (it may be finetuned).
#         }
#         params_dict = dict(self.named_parameters())
#         for name, loaded_weight in hf_model_weights_iterator(
#             model_name_or_path, cache_dir, load_format, revision
#         ):
#             if "projector" in name or "vision_tower" in name:
#                 for weight_name, param_name in projector_weights.items():
#                     if weight_name in name:
#                         name = name.replace(weight_name, param_name)
#                 param = params_dict[name]
#                 weight_loader = getattr(param, "weight_loader", default_weight_loader)
#                 weight_loader(param, loaded_weight)

#         # load language model
#         self.language_model.load_weights(
#             model_name_or_path, cache_dir, load_format, revision
#         )

#         monkey_path_clip_vision_embed_forward()


# class YiVLMultiModalProjector(nn.Module):
#     def __init__(self, config: LlavaConfig):
#         super().__init__()

#         self.linear_1 = nn.Linear(
#             config.vision_config.hidden_size, config.text_config.hidden_size
#         )
#         self.ln_1 = nn.LayerNorm(config.text_config.hidden_size)
#         self.act = nn.GELU()
#         self.linear_2 = nn.Linear(
#             config.text_config.hidden_size, config.text_config.hidden_size
#         )
#         self.ln_2 = nn.LayerNorm(config.text_config.hidden_size)

#     def forward(self, image_features):
#         hidden_states = self.linear_1(image_features)
#         hidden_state = self.ln_1(hidden_states)
#         hidden_states = self.act(hidden_states)
#         hidden_states = self.linear_2(hidden_states)
#         hidden_states = self.ln_2(hidden_states)
#         return hidden_states


# EntryClass = YiVLForCausalLM
